Download full-text


Available from: Tee Connie, Sep 27, 2015
235 Reads
  • Source
    • "System based on palmprint has high user acceptably [18]. Furthermore, palmprint also serves as a reliable biometric features because the print patterns are not same even in mono zygotic twins [5]. Limited work has been reported on palmprint based identification/verification despite of its significant features. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes a palmprint based verification system which uses low-order Zernike moments of palmprint sub-images. Euclidean distance is used to match the Zernike moments of corresponding sub-images of query and enrolled palmprints. These matching scores of sub-images are fused using a weighted fusion strategy. The proposed system can also classify the sub-image of palmprint into non-occluded or occluded region and verify user with the help of non-occluded regions. So it is robust to occlusion. The palmprint is extracted from the acquired hand image using a low cost flat bed scanner. A palmprint extraction procedure which is robust to hand translation and rotation on the scanner has been proposed. The system is tested on IITK, PolyU and CASIA databases of size 549, 5239 and 7752 hand images respectively. It performs with accuracy of more than 98%, and FAR, FRR less than 2% for all the databases.
    Telecommunication Systems 08/2011; 47(3):275-290. DOI:10.1007/s11235-010-9318-y · 0.71 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Currently, single sample biometrics recognition (SSBR) has emerged as one of the major research contents. It may lead to bad recognition result. To solve this problem, we present a novel approach by fusing two kinds of hand-based biometrics, i.e., palmprint and middle finger. We obtain their discriminant features by combining statistical information and structural information of each modal which are extracted using locality preserving projection (LPP) based on wavelet transform (WT). In order to reduce the influence of affine transform, we utilize mean filtering to enhance the robustness of structural information to improve the discriminant ability of palmprint high-frequency sub-bands. The two types of features are then fused at score level for the final hand-based SSBR. The experiments on the hand image database that contains 1,000 samples from 100 individuals show that the proposed feature extraction and fusion methods lead to promising performance. KeywordsSingle sample biometrics recognition–Palmprint and middle finger biometrics–Wavelet transform–Structural feature enhancement–Feature fusion
    Neural Computing and Applications 11/2011; 21(8):1-10. DOI:10.1007/s00521-011-0521-x · 1.57 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a bimodal biometric recognition system based on the extracted features of the human palmprint and iris using a new graph-based approach termed Fisher locality preserving projections (FLPP). This new technique employs two graphs with the first being used to characterize the within-class compactness and the second dedicated to the augmentation of the between-class separability. By applying the FLPP, only the most discriminant and stable palmprint and iris features are retained. FLPP was implemented on the frequency domain by transforming the extracted region of interest extraction of both biometric modalities using Fourier transform. Subsequently, the palmprint and iris features vectors obtained are matched with their counterpart in the templates databases and the obtained scores are fused to produce a final decision. The proposed combination of palmprint and iris patterns has shown an excellent performance compared to unimodal palmprint biometric recognition. The system was evaluated on a database of 108 subjects and the experimental results show that our system performs very well and achieves a high accuracy expressed by an equal error rate of 0.00%.
    Journal of Real-Time Image Processing 09/2013; 8(3). DOI:10.1007/s11554-011-0230-9 · 2.02 Impact Factor
Show more